Hoping for optimality or designing for inclusion

Persistence, learning, and the social network of citizen science

Julia K. Parrish, Timothy Jones, Hillary K. Burgess, Yurong He, Lucy F Fortson, Darlene Cavalier

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The explosive growth in citizen science combined with a recalcitrance on the part of mainstream science to fully embrace this data collection technique demands a rigorous examination of the factors influencing data quality and project efficacy. Patterns of contributor effort and task performance have been well reviewed in online projects; however, studies of hands-on citizen science are lacking. We used a single hands-on, out-of-doors project-the Coastal Observation and Seabird Survey Team (COASST)-to quantitatively explore the relationships among participant effort, task performance, and social connectedness as a function of the demographic characteristics and interests of participants, placing these results in the context of a meta-analysis of 54 citizen science projects. Although online projects were typified by high (>90%) rates of one-off participation and low retention (<10%) past 1 y, regular COASST participants were highly likely to continue past their first survey (86%), with 54% active 1 y later. Project-wide, task performance was high (88% correct species identifications over the 31,450 carcasses and 163 species found). However, there were distinct demographic differences. Age, birding expertise, and previous citizen science experience had the greatest impact on participant persistence and performance, albeit occasionally in opposite directions. Gender and sociality were relatively inconsequential, although highly gregarious social types, i.e., “nexus people,” were extremely influential at recruiting others. Our findings suggest that hands-on citizen science can produce high-quality data especially if participants persist, and that understanding the demographic data of participation could be used to maximize data quality and breadth of participation across the larger societal landscape.

Original languageEnglish (US)
Pages (from-to)1894-1901
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number6
DOIs
StatePublished - Feb 5 2019

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Social Support
Learning
Task Performance and Analysis
Demography
Observation
Meta-Analysis
Growth
Data Accuracy
Surveys and Questionnaires

Keywords

  • Citizen science
  • Crowdsourcing
  • Dabblers
  • Data quality
  • Retention

Cite this

Hoping for optimality or designing for inclusion : Persistence, learning, and the social network of citizen science. / Parrish, Julia K.; Jones, Timothy; Burgess, Hillary K.; He, Yurong; Fortson, Lucy F; Cavalier, Darlene.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 116, No. 6, 05.02.2019, p. 1894-1901.

Research output: Contribution to journalArticle

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